Research Scientist Intern, FAIR - Physical Engineering AI (PhD)

Meta's Fundamental AI Research (FAIR) lab is seeking Research Interns to join our team, which drives foundational research aimed at advancing physical engineering AI capabilities. Our goal is to generate advanced engineering designs such as robotic hardware, vehicles, and novel semiconductors. This internship involves heavy software research engineering, including large-scale data manipulation and simulator integration. Our interns have the opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.

We're especially interested in candidates with expertise across physical engineering (mechanical, electrical and aerospace engineering) design, simulation, reinforcement learning, and LLM post-training, including:

Novel post-training paradigms for LLMs using reinforcement learning
LLM post-training recipes using 3D data
Integration of large-scale simulation into LLM post-training
Computer vision (3D), representation learning, and self-supervised learning
Modeling for the physical world across mechanical, aerospace, civil, and other engineering domains

Our team at Meta AI offers twelve (12) to twenty-four (24) weeks long internships and we have various start dates throughout the year. To learn more about our research, visit https://ai.meta.com/research/.

Responsibilities

Explore and develop novel post-training paradigms for LLMs using reinforcement learning.
Explore and develop novel LLM post-training recipes using 3D data.
Integrate large-scale simulation into LLM post-training.
Explore mechanical, aerospace, civil, and other engineering disciplines and how to enable LLMs to solve key problems in these domains.
Brainstorm with research mentors, review literature and existing solutions of a challenging real-world research problem.
Develop novel solutions, implement prototypes, and perform extensive experiments to test the proposed solutions in meaningful benchmarks and metrics, analyze the results and verify the conclusions.
Draft and polish research reports and/or publications.
Present research outcomes to internal and/or external audiences.
Contribute research that can be applied to Meta product development.

Qualifications

Currently has or is in the process of obtaining a PhD degree in Artificial Intelligence, Physical AI, Computer Vision (3D), Machine Learning, Computer Science, Computer Engineering, Mechanical Engineering, Electrical Engineering, relevant technical field, or equivalent practical experience
Research and/or work experience in at least one of the following research areas: physical AI, simulation, reinforcement learning, representation learning, self-supervised learning, multimodal learning, robotics policy development, computer vision (3D), egocentric perception, embodied AI and/or LLMs, control theory, optimization algorithms
Experience in C/C++ and Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Experience integrating and debugging prototype/scientific software-hardware systems including mechanical, aerospace, or civil engineering domain-specific simulation
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, AAAI, JMLR and Computer Vision (CVPR, ICCV, ECCV, TPAMI)
Prior research or work experience in the fields of mechanical, aerospace, civil engineering or other engineering domains
Experience advancing AI, including contributions to open source libraries and frameworks
Experience manipulating and analyzing complex, large scale, high-dimensionality data from varying sources
Experience in utilizing theoretical and empirical research to solve problems
Experience working and communicating cross functionally in a team environment
Intent to return to degree-program after the completion of the internship/co-op